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481.
柴少明 《现代远程教育研究》2012,(4):35-40
计算机支持的协作学习(CSCL)是近年来教育技术和学习科学研究的热点,协作知识建构是CSCL的主要学习方式和学习目标,如何支持和促进协作知识建构是教育研究者和实践者关注的焦点问题之一。在社会建构主义和对话学习理论基础上建立的基于对话的教学设计和基于研究与实践的协作学习过程模型可以作为一种系统的教学模式,引入到CSCL学习环境中来支持协作知识建构。网络环境下协作学习困难和问题调查,以及CSCL课程实践证明:旨在提高学生对话能力,促进学生不同类型对话产生和发展,培养学生批判性思维能力的促进协作知识建构的教学设计和实施策略是有效的,学生在协作学习中能掌握并运用这些策略来促进协作学习。实现知识建构的目标。 相似文献
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本文采用文献研究和因子分析法建立评价指标体系,选取我国生态治理中具有代表性的区域--陕西省,运用DEA-Malmqusit方法测算了2004至2014年间生态经济治理优化决策效率。采用output-SBM模型对2014年我国31个省份的生态经济治理优化决策效率进行测算。结果表明,陕西省的生态经济治理优化决策效率呈下降趋势;中、西部省份生态经济治理优化决策效率整体不及东部省份的效率值;东部省份在森林蓄积量和保护区面积占辖区面积比重两个产出指标存在产出不足现象,中、西部省份在人均水资源量和工业废水排放量两个产出指标存在产出不足现象。 相似文献
485.
Qi Xia Thomas K. F. Chiu Ching Sing Chai Kui Xie 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(4):967-986
The anthropomorphic characteristics of artificial intelligence (AI) can provide a positive environment for self-regulated learning (SRL). The factors affecting adolescents' SRL through AI technologies remain unclear. Limited AI and disciplinary knowledge may affect the students' motivations, as explained by self-determination theory (SDT). In this study, we examine the mediating effects of needs satisfaction in SDT on the relationship between students' previous technical (AI) and disciplinary (English) knowledge and SRL, using an AI conversational chatbot. Data were collected from 323 9th Grade students through a questionnaire and a test. The students completed an AI basic unit and then learned English with a conversational chatbot for 5 days. Confidence intervals were calculated to investigate the mediating effects. We found that students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot, and that satisfying the need for autonomy and competence mediated the relationships between both knowledge (AI and English) and SRL, but relatedness did not. The self-directed nature of SRL requires heavy cognitive learning and satisfying the need for autonomy and competence may more effectively engage young children in this type of learning. The findings also revealed that current chatbot technologies may not benefit students with relatively lower levels of English proficiency. We suggest that teachers can use conversational chatbots for knowledge consolidation purposes, but not in SRL explorations.
Practitioner notes
What is already known about this topic- Artificial intelligence (AI) technologies can potentially support students' self-regulated learning (SRL) of disciplinary knowledge through chatbots.
- Needs satisfaction in Self-determination theory (SDT) can explain the directive process required for SRL.
- Technical and disciplinary knowledge would affect SRL with technologies.
- This study examines the mediating effects of needs satisfaction in SDT on the relationship between students' previous AI (technical) and English (disciplinary) knowledge and SRL, using an AI conversational chatbot.
- Students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot.
- Autonomy and competence were mediators, but relatedness was not.
- Teachers should use chatbots for knowledge consolidation rather than exploration.
- Teachers should support students' competence and autonomy, as these were found to be the factors that directly predicted SRL.
- School leaders and teacher educators should include the mediating effects of needs satisfaction in professional development programmes for digital education.
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